Bayesian Optimization not implemented as a tuner

[goergen95]

The function for bayesian optimization points to an oracle rather than the implemented tuner. Is there a reason for this I am not aware and would it be possible to implement the function in a way that the tuner is directly available without the need to write a custom tuner?

I imagine the function to point to the tuner like this:

  do.call(kerastuner$tuners$BayesianOptimization,args)

rather than its current implementation pointing to the oracle:

  do.call(kerastuner$oracles$BayesianOptimization,args)

[turgut090]

Hi. This is probably due to this example: https://keras-team.github.io/keras-tuner/tutorials/subclass-tuner/

would it be possible to implement the function in a way that the tuner is directly 
available without the need to write a custom tuner?

Yes, but if you have an option, please open a PR.
In my opinion, there could be done the following:

1. oracles$BayesianOptimization parameters are:

    objective = objective,
    max_trials = max_trials,
    num_initial_points = num_initial_points,
    alpha = alpha,
    beta = beta,
    seed = seed,
    hyperparameters = hyperparameters,
    allow_new_entries = allow_new_entries,
    tune_new_entries = tune_new_entries

2. tuners$BayesianOptimization parameters almost the same. But with 1 additional parameter:

hypermodel = hypermodel,

What could you do?

You could add all the parameters of tuners$BayesianOptimization (current state) + 1 additional parameter hypermodel. And then if hypermodel parameter is missing,
try to call oracle. Otherwise, the tuner.

What do you think?

[goergen95]

Thanks for the quick reply.
Thats basically what I tried with a PR from earlier today. Somehow it failed every single check so I thought to create this issue to figure out if something I am not aware of is going wrong.

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